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Multivariable adaptive control of unknown nonlinear dynamic systems using neural networks

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2 Author(s)
Dianhui Wang ; Res. Center of Autom., Northeastern Univ., Shenyang, China ; Tianyou Chai

Based on the approximation capability and generalization property of the backpropagation neural networks (BPNNs) for nonlinear mapping on a compact set, this paper presents a novel approach for designing adaptive neurocontroller (ANC) for unknown multivariable discrete nonlinear systems in general form. The key ideas of the proposed control strategy are of applying the Clarke's one-step-ahead weighted predictive control performance index and linearizing the feedforward BPNNs identifier models. Simulation results demonstrate the effectiveness of the new adaptive neural control scheme

Published in:

Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on  (Volume:3 )

Date of Conference:

14-16 Dec 1994